GIS-Based Approach to Identify Climatic Zoning: a Hierarchical
Total Page:16
File Type:pdf, Size:1020Kb
GIS-based approach to identify climatic zoning: A hierarchical clustering on principal component analysis Jean-Philippe Praene, Bruno Malet-Damour, Mamy Harimisa Radanielina, Ludovic Fontaine, Garry Riviere To cite this version: Jean-Philippe Praene, Bruno Malet-Damour, Mamy Harimisa Radanielina, Ludovic Fontaine, Garry Riviere. GIS-based approach to identify climatic zoning: A hierarchical clustering on principal component analysis. Building and Environment, Elsevier, 2019, 164, pp.106330. 10.1016/j.buildenv.2019.106330. hal-02271933 HAL Id: hal-02271933 https://hal.univ-reunion.fr/hal-02271933 Submitted on 27 Aug 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. GIS-based approach to define climatic zoning : A hierarchical clustering on principal component analysis a,∗ a b Jean Philippe Praene , Bruno Malet-Damour , Mamy Harimisa Radanielina , Ludovic a c Fontaine , Garry Rivie`re aPIMENT Laboratory - University of la Reunion, 117 rue du General Ailleret - 97430 le Tampon - Reunion bInstitute for the Management of Energy (IME), Po. Box 566, University of Antananarivo, Madagascar cBuilding Sciences and Environment Department - University of la Reunion, 117 rue du General Ailleret - 97430 le Tampon - Reunion Abstract In tropical environments, the design of bioclimatic houses adapted to their environment is a crucial issue when considering comfort and limiting energy needs. A preliminary part of such design is an accurate knowledge of the climatic conditions in each region of the studied territory. The objective of this paper is to propose climatic zoning from a database of 47 meteorological stations in Madagascar by investigating hierarchical clustering on principal components. Then, theses results are combined with a spatial interpolation using a Geographic Information System approach. This step allows us to define three climatic zones corresponding to dry, humid and highland zones. These results make it possible to define standard meteorological files that are used to evaluate the thermal performance of traditional Malagasy houses. Regardless of the type of house and the areas considered, the percentage of comfort, according to Givoni bioclimatic chart, varies from an average value of 20 % to 70 % without ventilation and with an air velocity of 1 m=s, respectively. It can be concluded that Madagascar's traditional habitat has adapted over time to the constraints of its environment. Keywords: Madagascar, Climate zone, Clustering, PCA, Givoni Bioclimatic Chart, GIS 1. Introduction Climatic zoning is an essential prerequisite for climate responsive building design [1{3]. The importance of an accurate knowledge of climate conditions for building energy efficiency simula- ∗Corresponding author, Tel. +262 692 235 566 Email address: [email protected] (Jean Philippe Praene) URL: piment.univ-reunion.fr (Jean Philippe Praene) Preprint submitted to Building and Environment August 27, 2019 tion is widely known. According to the World Energy Outlook 2018 by IEA1, the world energy consumption for building sector was 3,047 Mtoe which accounted for 31.4 % of the total final consumption in 2017 [4]. Environmental issues are at the forefront of regulatory requirements. Taking into account both the energy and environmental performance of buildings is a logical ap- proach which will become widespread and the rule for all in the future. For developing countries like Madagascar, these issues are all the more important because they can weaken or boost the country's development. By 2020, developing and emerging countries will be more energy-intensive than developed countries, [5]. Thus minimizing energy demand in the construction sector through building in a climate-resilient manner is an appropriate option to decrease their energy vulnerabil- ity due to fossil fuel imports. Like many developing countries, Madagascar is experiencing rapid urbanization. Out of a total population of 25.57 million (2017), the country has now nearly 7 million urban dwellers, compared to 2.8 million in 1993. In 20 years, the combined effect of population growth, rural exodus and in- terurban migration to the capital have led to a 50 % increase in building construction. The national energy balance 2017 of Madagascar [6] shows that the residential sector represents 3,245 ktoe that is 59 % of the final energy consumption. Urban areas must therefore face the challenge to sustain and mitigate energy consumption due to urban population growth and economic development [7]. One of the possible actions would be to build buildings that are adapted to their environment and therefore low in energy consumption. The purpose of this research is to investigate a new approach to define climatic zoning in the case of low data availability. Our approach is based on a combination of zoning from GIS interpolation coupled with clustering. Another objective of the study is to update the Malagasy climatic zoning by redefining the geographical boundaries of climatic zones based on multivariate data analysis. Finally , to complete the zoning objective, the results are then applied to traditional houses to evaluate their thermal comfort performance. Finally, the illustration of this zoning will allow the evaluation of the thermal comfort of traditional Malagasy houses and also the definition of typical meteorological files. 1International Energy Agency 2 1.1. State-of-the-art in climate zoning There are different ways to identify climatic zones based on different criteria using clustering methods (statistical analysis by group observation and analysis of possible groupings, also called \modern methods") [8] or class methods (with the use of thresholds for climate variables and indices, also called traditional methods) [8]. The selection of the method largely depends on the objective of the climate classification. Among the most recognized classifications based on class method, the K¨oppen-Geiger classification is often considered as a reference in the field and supports many multidisciplinary studies [9, 10]. This classification established climatic zones based on natural vegetation cover. K¨oppen decomposed the zones into five climatic zones: an equatorial zone (A); an arid zone (B); a temperate warm zone (C); a snow zone (D); and a polar zone (E). The classification added nuances through second and third letters related to precipitation and temperature. K¨oppen classification is a powerful classification for global analysis [11]. It is often illustrated as being a diagnostic tool to monitor climate change on different time scales and for different aspects. It was used to highlight the effect of climate change on ecosystems, energy consumption or climate variability at different time scales [12{15]. This method was not unanimously accepted when used for other purposes. Many authors showed that in specific use cases this approach has limitations. For a local problem, other methods were more precise and more consistent with the identified climate zones : clustering methods. A comparative study was carried out by Zscheischler [16]. This author proposed to compare the accuracy of the K¨oppen- Geiger classification to that of principal component analysis (PCA) using the "k-means" clustering method. The study verified that climate and vegetation variables constructed similar groups and then showed that the parameters used in the K¨oppen-Geiger classification are not optimal for categorizing a climate. The use of clustering based on meteorological data allows better results to be achieved. Other comparisons were also conducted in recent years [17{19] and some even showed that K¨oppen did not allow to obtain specific information necessary for the problem of building design and thermal comfort [20{22]. Other class methods also used for climate classification to study comfort in the building [3, 20, 22] or for climate classification of urban and rural sites [23{26]. Multivariable statistical analysis based on clustering methods makes it possible to obtain an efficient climate classification [27] and seem more coherent for building concerns [8]. Other studies confirmed the interest of clustering [28, 29] and specifically of k-means clustering with Euclidean distance 3 correlation as a measure of similarity for the classification of a climate in general [16, 18] or adapted to a building [1, 2, 22, 30{33]. The quality and availability of the parameters used for climate classification are essential. The literature reveals that many parameters, from various origins, make it possible to guide the climate classification according to its final objective. The representativeness of the data in the climate analysis is a significant criterion, especially for clustering methods. Clustering methods or class methods use (i) climate data (outdoor air temperature ; outdoor relative humidity ; global solar irradiation; precipitation; altitude; wind velocity and direction; atmospheric pressure) [20, 21, 27] (ii) climate indexes (sky clearness index kt) [34] (iii) topographic parameters [34] or (iiii) thermal comfort indexes (Terjung's comfort index [35, 36] ; Physiological